Since one of the biggest causes of traffic accidents is drowsy driving, many methods for preventing drowsy driving have been researched recently. Conventional technology of determining drowsiness of a driver includes photographing a motion of the driver's pupils using a vehicle signal (e.g., steering signal, width position in a lane using a camera, etc.), photographing the motion of the driver's pupils using a camera photographing a driver, and detecting an opening and closing interval of eyes.
However, it is difficult for the vehicle signal to distinguish between intentional vehicle behavior (i.e., intentionally erratic driving or driving in response to a sudden change in surrounding environment) and driving patterns due to drowsiness. It is also difficult for a driver image-based scheme to accurately determine fatigue drowsiness based on the recognized opening and closing of the driver's eyes in certain photo-environments and during unexpected conditions, such as when wearing glasses and laughing.